Papers with Code 아카이브

Computational Efficiency

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 1
보존된 평가 기록: 1

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ai4cts/lightcts

공식 코드

LightCTS: A Lightweight Framework for Correlated Time Series Forecasting

https://github.com/aida-ugent/topoembedding

공식 코드

Topologically Regularized Data Embeddings

https://github.com/aiha-lab/rilq

공식 코드

RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model Accuracy

https://github.com/aitical/scnet

공식 코드

Fully $1\times1$ Convolutional Network for Lightweight Image Super-Resolution

https://github.com/alanjohnvarghese/TransformerG2G

공식 코드

TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers

https://github.com/albert-jin/cvt-assd

공식 코드

CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox Detector

https://github.com/alexlimh/cec-ranking

공식 코드

Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking

https://github.com/alexyangxx/colormamba

공식 코드

ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba

https://github.com/alfredofernandez2021/TeLU-Experiments

공식 코드

TeLU Activation Function for Fast and Stable Deep Learning

https://github.com/alibaba/AliceMind/tree/main/mPLUG

공식 코드

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

https://github.com/alibaba/lightweight-neural-architecture-search

공식 코드

Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition

https://github.com/alibahri94/smart-pc

공식 코드

SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds

https://github.com/aliceqingyangwang/weightpolarityexpr

공식 코드

Polarity is all you need to learn and transfer faster

https://github.com/alirezaabdollahpour/superdeepfool

공식 코드

Revisiting DeepFool: generalization and improvement

https://github.com/allen4747/Ferret

공식 코드

Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models

https://github.com/alpha-vl/convmae

공식 코드

ConvMAE: Masked Convolution Meets Masked Autoencoders

https://github.com/alwaysfhao/tim4rec

공식 코드

TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality Model

https://github.com/amd-aig-aima/amd-hummingbird-t2v

공식 코드

AMD-Hummingbird: Towards an Efficient Text-to-Video Model

https://github.com/amir-cardiolab/pcnde

공식 코드

Physics-constrained coupled neural differential equations for one dimensional blood flow modeling

https://github.com/amirrezafateh/multi-scale-transformer-pneumonia

공식 코드

Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach

https://github.com/amodas/PRIME-augmentations

공식 코드

PRIME: A few primitives can boost robustness to common corruptions

https://github.com/anakin2555/decomposewhar

공식 코드

Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition

https://github.com/ancientshi/karsein

공식 코드

CTR-KAN: KAN for Adaptive High-Order Feature Interaction Modeling

https://github.com/andrea-cavallo-98/sparse-vnn

공식 코드

Sparse Covariance Neural Networks

https://github.com/andreagrisafi/SALTED

공식 코드

Electronic-structure properties from atom-centered predictions of the electron density

https://github.com/andreascharalamp/fastfeedforward-experiments

공식 코드

Enhancing Fast Feed Forward Networks with Load Balancing and a Master Leaf Node

https://github.com/andruekonst/hrbm

공식 코드

Interpretable Ensembles of Hyper-Rectangles as Base Models

https://github.com/angie-lab-jhu/density_softmax

공식 코드

Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts

https://github.com/angie-lab-jhu/neuralvarlinucb

공식 코드

Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits

https://github.com/angrycai/flgc

공식 코드

Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering

https://github.com/angusmonroe/unionsnn

공식 코드

Union Subgraph Neural Networks

https://github.com/anikat1/sam-material-gan

공식 코드

Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing

https://github.com/anikpram/Deep-SLR

공식 코드

Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)

https://github.com/annalena-k/FAB-meets-diffME

공식 코드

Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics

https://github.com/annkristinbalve/Interpretable_Breast_Cancer_Classification

공식 코드

Interpretable breast cancer classification using CNNs on mammographic images

https://github.com/anoise/distpred

공식 코드

DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.